A Framework for Identifying and Analyzing Drivers Affecting the Future of the Retail Industry with a Focus on Human Factors (Case Study: Hyper Family Chain Stores)

Subject Areas : Strategic Management Researches

Ali Mohaghar ¹ , mohamad hasan maleki ² , Seyed Milad Seyed Javadein ³

1 - Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
2 - Associate Professor, Department of Management, Faculty of Management and Economics, Qom University, Qom, Iran.
3 - PhD student of Industrial Management, Kish Campus, University of Tehran, Tehran, Iran.

Received: 2023-05-25 Accepted : 2023-07-01 Published : 2023-09-21

Keywords: Human Factors, drivers, Chain Stores, future, Retail Industry,

Abstract :

Objective: The retail industry and chain stores play an important role in the economic development of countries and the efficiency of the supply chain. Human factors are one of the drivers that will completely change the future of this industry. The current research seeks to identify and analyze the drivers affecting the future of hyper family chain stores, focusing on human factors.Method: The current research is applied in terms of orientation and pragmatic in terms of philosophical foundations. In this research, various quantitative (Fuzzy Delphi and Copras) and qualitative (focus group and root definitions tool) methods were used for data analysis. The theoretical community of the research was the managers and senior consultants of human resources management in hyper family. Sampling was done as a judgment based on the expertise of experts in the field of retail industry. The sample size was equal to 10 people.Findings: 30 drivers were extracted through literature review and structured interviews with experts. Then expert assessment questionnaires were distributed among the experts and the data were analyzed using the fuzzy Delphi method. 13 drivers had a de-fuzzy number higher than 0.7 and were selected for final prioritization with Copras. The two drivers of data-driven decision making and task automation had the highest priority.Conclusion: The research scenarios were developed based on two priority drivers considering the six components of the root definitions tool. Some of the suggestions of the research were: using data-based technologies such as business intelligence and big data for human resource management processes, developing a data-based decision-making culture and moving towards more automation in various human resource management processes such as performance evaluation and recruitment and hiring.

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